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Abstract

This paper deals with the problem of neural code solving. On the basis of the formulated
hypotheses, the information model of a neuron detector is suggested, the detector
being one of the basic elements of an artificial neural network (ANN). The paper subjects
the connectionist paradigm of ANN building to criticism and suggests a new presentation
paradigm for ANN building and neuro-elements (NE) learning. The adequacy of the suggested
model is proved by the fact that it does not contradict the modern propositions of
neuropsychology and neurophysiology.

During learning and development, the level of synaptic input received by cortical neurons may change dramatically. Given a limited range of possible firing rates, how do neurons maintain responsiveness to both small and large synaptic inputs? We demonstrate that in response to changes in activity, cultured cortical pyramidal neurons regulate intrinsic excitability to promote stability in firing. Depriving pyramidal neurons of activity for two days increased sensitivity to current injection by selectively regulating voltage-dependent conductances. This suggests that one mechanism by which neurons maintain sensitivity to different levels of synaptic input is by altering the function relating current to firing rate.

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